Machine Learning-Assisted design of boron and nitrogen doped graphene nanosheets with tailored thermomechanical properties

Amin Hamed Mashhadzadeh, Maryam Zarghami Dehaghani, Amir Hamed Mashhadzadeh, Aidyn Kadyr, Boris Golman, Christos Spitas, Konstantinos V. Kostas

Research output: Journal PublicationArticlepeer-review


Doping graphene with boron and/or nitrogen enhances its potential for various applications, such as electronics and energy devices. However, these modifications impact the material's properties, influencing its response to external forces, temperature, and heating. Investigating the connection between the structure of co-doped graphene nanosheets and their thermomechanical properties is crucial for accurate predictions and tailored nanosheet engineering. In the present study, molecular dynamics (MD) simulations were performed to compute the thermal and mechanical properties of 300 randomly generated boron/nitrogen co-doped graphene nanosheets with doping concentrations in the range of 0%-15%. Next, the obtained MD results were used to train, tune, and validate a wide spectrum of supervised machine learning models, including linear regression models, regression trees, support vector machines, Gaussian process regression models, kernel approximation models, ensembles of regression trees, and regression neural networks. Our aim is to find models that better estimate, without overfitting, the structure-properties relation of boron/nitrogen co-doped nanosheets. To this end, the models with the highest coefficient of determination (R2) and the lowest root mean square error (RMSE) were utilized. Finally, an appropriate implementation of guided random search algorithms, mainly Genetic Algorithms, was used to solve the inverse problem, i.e., find the nanosheet structure which exhibits the desired set of property values. To this end, thermal conductivity, Young's modulus, failure stress, and failure strain were specified as target values. A detailed comparison between the employed models and their validation via MD simulations is also included. The achieved results provide sufficient evidence of the accuracy and viability of the applied procedure in both prediction of thermomechanical properties as well as identification of structures with desired properties. This study provides a roadmap that could help scientists and engineers develop nanosheets-based materials with tunable thermomechanical properties, ultimately leading to the creation of innovative and high-performance technologies.

Original languageEnglish
Article number112998
JournalComputational Materials Science
Publication statusPublished - May 2024


  • Doping
  • Graphene
  • Machine Learning
  • Molecular Dynamics Simulation
  • Thermomechanical Properties

ASJC Scopus subject areas

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics


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